Business entities commonly use their customers' profiles as a basis for marketing or other business actions. For example, business actions such as targeted marketing mailings, generating opportunities for cross-selling, loyalty modeling and fraud detection are advantageously based on profiles of customer behavior or preferences. Using conventional profiling methods, the customer profiles are developed by first accumulating customer information in data warehouses. The information in the data warehouses may be accumulated over months or years. The data warehouses then are intermittently accessed to analyze the accumulated customer information for developing the customer profiles. Data mining techniques may be employed to find useful or actionable knowledge in the data warehouses for initiating targeted marketing. See e.g., “The Man who knows Too Much,” Forbes, Nov. 11, 2002. Further, for example, Culhane U.S. Pat. No. 6,513,018 B1, which is assigned to Fair, Isaac and Company Inc., San Rafael, Calif., describes a data mining technique, which can be used to obtain customer credit scores from historic customer performance data stored in databases. However even with all available data mining techniques, it is difficult to extract actionable knowledge from the data warehouses in a timely manner. The difficulty may at least in part stem from the awkward or varied formats that historically have been used to store customer information in the data warehouses. Further, the customer profiles obtained by conventional profiling methods are stale as they are often based on antiquated information, which is accumulated in the data warehouses. Use of stale customer profiles can lead to inefficient or unproductive business actions such as improperly targeted marketing actions.
In the context of Internet commerce, Sterling U.S. Pat. No. 6,466,975 B1 (“Sterling”), which is assigned to Digital Connexxions Corp., Oakville, Calif., describes use of an artificial intelligence system for personalized marketing efforts directed toward repeat visitors to an Internet web site. As described by the assignee, Sterling's artificial intelligence system may be used to dynamically tailor marketing efforts by learning from the responses of the web site visitors to previous marketing efforts. See e.g., “Digital Connexxions Awarded U.S. Patent for its Innovative Predictive Marketing Technology,” Press Release, Feb. 18, 2003, http://www.dconx.com/news.html.
Consideration is now being given to ways of enhancing systems and methods for customer profiling to obtain more current and timely customer profiles. In particular, attention is directed to systems and methods for developing timely customer profiles based on current credit card transactions performed by the customers.